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Data! Action! Data journalism issues to watch in the next 10 years

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Keynote at the Nordic data journalism conference #NODA16 - an outline of issues facing data journalism which journalists and academics need to focus on in the next decade.

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Data! Action! Data journalism issues to watch in the next 10 years

  1. 1. ACTION! DATA! 11 @PaulBradshaw MA Online Journalism Birmingham City University
  2. 2. 2 “We are moving from the knowledge/power nexus portrayed by Foucault to a data/action nexus that does not need to move through theory: All it needs is data together with preferred outcomes” “ Geoffrey Bowker
  3. 3. Shangri-La
  4. 4. 
 What is journalism for?
  5. 5. Independence from data
  6. 6. http://www.independent.co.uk/student/student-life/Studies/half-of-uk-university-students-are-losing-marks- for-not-referencing-correctly-survey-finds-a6983716.html
  7. 7. First duty? Public debate?
  8. 8. 12 Are news values changing?
  9. 9. https://www.youtube.com/watch?v=UNQteT9Bu2w&t=2m
  10. 10. Voice to the voiceless
  11. 11. Who has a voice in data?
  12. 12. Who has a voice in data? How is data used as a proxy for the voiceless?
  13. 13. Who has a voice in data? How is data used as a proxy for the voiceless? How is data used to amplify voices?
  14. 14. First duty? Public debate? http://www.theguardian.com/news/datablog/2014/oct/29/todays-key-fact-you-are-probably-wrong-about-almost-everything
  15. 15. Truth to power
  16. 16. 24 DATA JOURNALISM ≠ INVESTIGATIVE JOURNALISM
  17. 17. 25 Using data to tell stories in the best possible way, combining the best techniques of journalism: including visualizations, concise explanation and the latest technology. It should be open, accessible and enlightening. “ Simon Rogers
  18. 18. Data dystopia
  19. 19. Data dystopia 39.8333333, -98.585522
  20. 20. http://www.buzzfeed.com/ryanhatesthis/who-watches-more-porn-republicans-or-democrats#.mtoR5PAWV
  21. 21. 32 Flowing Data’s NYC map offers a great example of how three mapping traps — seasonal bias, location fuzziness, and tagging bias — can lead to a map that does not reflect reality. “ Margaret McKenna, 

  22. 22. http://fivethirtyeight.com/datalab/mapping-kidnappings-in-nigeria/
  23. 23. 35 Data journalism is like sex at university – everyone talks about it; few do it; fewer still do it well.”“ Neil McIntosh

  24. 24. Acquisition Cleaning Loading Verification Analysis Cherry-picking Presentation MaintenanceJake Harris
  25. 25. 37 DATA ≠ TRUTH
  26. 26. 38 ACTION! DATA!
  27. 27. …Bad data
  28. 28. https://theintercept.com/drone-papers/firing-blind/
  29. 29. 41 When a measure becomes a target, it ceases to be a good measure”.“ Goodhart’s Law

  30. 30. *
  31. 31. 45 “My husband couldn’t understand why people were taunting him and calling him a child molester” “ Texas Tribune
  32. 32. “Statistics are like a bikini…”
  33. 33. 47 Statistics are like a bikini. What they reveal is suggestive, but what they conceal is vital.”“ Prof. Aaron Levenstein
 http://charonqc.wordpress.com/2011/03/27/guest-post-any-complaints-why-the-ipcc-is-failing-us-all/
  34. 34. http://www.bloomberg.com/news/articles/2011-12-01/correlation-or-causation
  35. 35. 52 HOLY MULTIPLE COMPARISONS PROBLEM!
  36. 36. 55 ALGORITHM DYNAMICS! BIG DATA HUBRIS!
  37. 37. * Abraham Wald http://www.motherjones.com/kevin-drum/2010/09/counterintuitive-world
  38. 38. Routes
  39. 39. Telling stories
  40. 40. 61 Having a simple data vis on the page increases dwell time by a third”“ Trinity Mirror Editorial Conference 2014
  41. 41. “Photos give the biggest lift with
 quotes coming in a close second. Tweets containing numbers, a video url and hashtags also lead to double-digit boost.” https://media.twitter.com/best-practice/news-the-impact-of-tweeting-with-photos-videos-hashtags-and-links
  42. 42. 64 The death of 1 man = tragedy, the death of millions is a statistic “ Not Stalin.
  43. 43. 65 Courts punish people less harshly when they harm more people.”“ Ben Goldacre
  44. 44. Andy Kirk, Data Visualisation
  45. 45. Robot journalism - or computational journalism?
  46. 46. 70 DRAMA! TECHNOLOGICAL!
  47. 47. http://nytlabs.com/projects/editor.html
  48. 48. Daniel Ellsberg + co: more than 1 year
  49. 49. Daniel Ellsberg + co: more than 1 year =18 years for Bradley Manning leaks
  50. 50. Daniel Ellsberg + co: more than 1 year =18 years for Bradley Manning leaks =2880 years for Offshore leaks
  51. 51. =28,800 years for Panama Papers
  52. 52. 77 If we don’t address software itself, we are in danger of always dealing only with its effects rather than the causes” “ Lev Manovich
  53. 53. Data integrity, journalist integrity
  54. 54. Fake adverts Spyware Backdoors App permissions Passwords Phishing
  55. 55. Rituals of transparency & culture shifts
  56. 56. Mark Coddington
  57. 57. http://www.buzzfeed.com/johntemplon/how-we-used-data-to-investigate-match-fixing-in-tennis#.eiR89lLA2
  58. 58. Wilson Lowrey http://www.tandfonline.com/doi/figure/10.1080/1461670X.2015.1052537 http://www.poynter.org/2016/there-are-96-fact-checking-projects-in-37-countries-new-census-finds/396256/
  59. 59. 84 “This clash of logics has required journalists to reshape forms and practices in order to regain lost legitimacy, and the fact checking site is a result of this reshaping [because it takes sides].” “ Wilson Lowrey
  60. 60. Lighthouses
  61. 61. 1. How independent? 2. How do we choose? 3. Who has a voice? 4. Myth vs truth? 5. What mistakes do we make? 6. How do we engage? 7. What are (and can) we automating? 8. How do we ensure integrity? 9. What cultures and why? 10. Data as power! Everybody likes a list
  62. 62. 87 ACTION! DATA!
  63. 63. 88 @PaulBradshaw Thank you._ MA Online Journalism Birmingham City University

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